Chaotic System Design of Swarm Intelligent Optimization Algorithm

As swarm intelligence algorithm has such problems as poor convergence performance, long search time, and low search efficiency and it is also easy to stagnate at locally optimal solution in the process to solve complex optimization problems, self-adaptation hybrid particle swarm optimization algorithm based on improvement of two-dimensional mapping henon is put forward, which provides the combination mechanism of chaotic mapping and particle swarm with benchmark standard test problem as test function to prove the effectiveness of algorithm. The aim is to elevate the performance of swarm intelligence and to increase its capacity to solve issues in complex optimization problems.